Ultrasound imaging system with pixel oriented processing

ABSTRACT

An ultrasound imaging system with pixel oriented processing is provided in which an acoustic signal is generated, echoes from the acoustic signal are received at a plurality of receiving elements to obtain echo signals that are then stored, a given pixel is mapped into a region of the stored signals, the mapped region of the stored echo signals is organized into array for the given pixel after which the array is processed to generate a signal response for the given pixel to obtain acoustic information for the given pixel. The system can be implemented entirely on plug-in cards for a commercial PC motherboard. The system and method can be implemented for pixel-oriented or voxel-oriented image processing and display, eliminating intermediate data computations and enabling extensive use of software processing methods. Advantages include improved acquisition of signal dynamic range, flexible acquisition modes for high frame rate 2D, 3D, and Doppler blood flow imaging.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is directed to an ultrasound imaging architectureand, more particularly, to a system and method of capturing andprocessing ultrasound data and generating images therefrom utilizingpixel oriented processing techniques.

2. Description of the Related Art

Ultrasound Imaging has developed into an effective tool for diagnosing awide variety of disease states and conditions. The market for ultrasoundequipment has seen steady growth over the years, fueled by improvementsin image quality and the capability to differentiate various types oftissue. Unfortunately, there are still many applications for ultrasoundsystems where the equipment costs are too high for significant adoption.Examples are application areas such as breast cancer detection, prostateimaging, musculoskeletal imaging, and interventional radiology. In theseareas and others, the diagnostic efficacy of ultrasound imaging dependson excellent spatial and contrast resolution for differentiation andidentification of various tissue types. These performance capabilitiesare found only on the more expensive ultrasound systems, which have moreextensive processing capabilities.

Ultrasound imaging has always required extensive signal and imageprocessing methods, especially for array systems employing as many as128 or more transducer elements, each with unique signal processingrequirements. The last decade has seen a transition to the improvedaccuracy and flexibility of digital signal processing in almost allsystems except for those at the lowest tiers of the market. Thistransition has the potential for reducing system costs in the long term,by utilizing highly integrated digital circuitry. Unfortunately, the lowmanufacturing volumes of ultrasound systems results in substantialoverhead and fixed costs for these unique circuits, and thus thetransition to digital signal processing has not significantly reducedsystem cost.

While ultrasound systems have increasingly adopting digital processingtechnology, their architectures have not changed significantly fromtheir analog counterparts. Almost all current systems on the market usea modular “flow-through” architecture, with signals and data flowingfrom one module to the next, as shown in FIGS. 1A and 1B. This is anatural method of dealing with the considerable complexity of ultrasoundimage formation and processing, and allows separate development teams towork somewhat independently on individual modules. FIG. 1A shows thethree types of information processing that are typically performed withultrasound systems—echo image processing, for normal 2D imaging; Dopplerprocessing, for blood velocity measurements; and color flow imageprocessing, for real-time imaging of blood flow.

A major disadvantage of the flow-through architecture is that eachmodule must wait on its input data from the previous module before itcan perform its own processing. The module must then deliver its resultto the next module. Even within the blocks shown in FIG. 1A, there aremany individual processing steps that are performed in series. Since therate of system processing is determined by the rate of slowestprocessing function in the chain, all processing blocks must perform athigh speed with minimal latencies, so as to not to introduce delays inseeing an image appear on the display as the scanhead is moved.

Another disadvantage of the flow-through architecture is that it makesinefficient use of resources. Most ultrasound exams are performedprimarily with 2D echo imaging only, with only occasional use of Dopplerblood velocity measurements or color flow imaging. This means that thecomplex and expensive hardware processing modules needed to performthese functions are sitting idle most of the time, as they cannot beused in other tasks.

BRIEF SUMMARY OF THE INVENTION

The disclosed embodiments of the present invention are directed to anultrasound imaging method and system that performs all signal processingand image formation in software executing on commercial CPUs. The onlycustom hardware required in this approach is for transmission ofacoustic pulses and data acquisition and signal conditioning of thereceived signals from the transducer. To accomplish this goal requiresfundamental changes in the processing architecture of the ultrasoundsystem to reduce the number of processing steps required in forming theimage and to eliminate system latencies. It also requires maximumutilization of the processing resources of the CPU to achieve theprocessing throughput desired. As an important benefit, the newarchitecture allows improvements in system dynamic range that open upthe possibility of utilizing new transducer materials in a low-costscanhead design. In addition, new modes of acquisition are possible thatmay provide significant new diagnostic information.

The disclosed software-based ultrasound system architecture leveragesthe high volume, low cost processing technology from the computerindustry by basing the design around a commercial computer motherboard.While some current ultrasound systems incorporate computer motherboardsin their design, the computer is used only for the user interface andsome system control and does not participate in any real-time processingtasks. In the disclosed architecture, the computer motherboard replacesalmost all existing hardware, rather than complementing it. Basing thesystem in software on a general-purpose platform provides a flexible,high-performance imaging system at the lowest possible system cost. Nocustom integrated circuits are required for this approach, reducingsystem complexity and time-to-market. Moreover, as further improvementsin CPU processing power are realized by the computer industry, they canbe easily adopted by the system to enhance imaging performance orprovide new modes of operation and information extraction.

The successful realization of the software-based ultrasound architecturerepresents a market breakthrough in the cost/performance ratio ofultrasound systems. Presumably, this can significantly increase theutilization of ultrasound in cost-sensitive applications that demandhigh image resolution and tissue differentiation for diagnosticefficacy. In addition, the low system cost and processing flexibilityshould open up new specialty application areas where ultrasound has notpreviously played a significant role.

In accordance with one embodiment of the invention, an ultrasoundprocessing method is provided that includes generating an acousticsignal, receiving at least one echo of the acoustic signal at aplurality of receiving elements and obtaining an echo signal therefrom,storing each echo signal from each of the plurality of receivingelements, mapping a given pixel into a region of the stored echosignals, organizing the mapped region of the stored echo signals into anarray for the given pixels, processing the array to generate a signalresponse for the given pixels, and using the signal response to obtainacoustic information for the given pixel.

In accordance with another aspect of the foregoing embodiment, aninitial step is provided that includes generating a set of given pixelschosen to represent an area in a field of view of the transducergenerating the acoustic signal, in which even given pixel in the arrayset has a known spatial relationship to the plurality of receivingelements. Preferably the method also includes generating an image fromthe acoustic information for the given pixels in the array.

In accordance with another aspect of the foregoing embodiment, theacoustic information can be used for one or more of the following,including, but not limited to, measuring and displaying spatial data,measuring and displaying temporal data, measuring and displaying bloodflow data, and measuring and displaying tissue displacement responsiveto induced mechanical displacement caused by an acoustic signal oracoustic transmit wave.

In accordance with another aspect of the foregoing embodiment, themethod includes generating a plurality of acoustic signals, receivingechoes from the plurality of acoustic signals, and combining thereceived echoes over multiple generating and receiving cycles to enhanceacoustic information obtained therefrom.

In accordance with another aspect of the foregoing embodiment, thestored echo signals are combined and averaged. Furthermore, the signalresponse comprises an average of the stored echo signals.

In accordance with another aspect of the foregoing embodiment, themethod includes combining results of multiple processing of the array toderive enhanced acoustic information.

In accordance with another aspect of the foregoing embodiment, theenhanced acoustic information includes spatial compounding that improvescontrast resolution of a final image generated therefrom. In addition,the combined signals are representative of Doppler informationassociated with moving tissue or moving blood cells.

In accordance with another aspect of the foregoing embodiment, thereceiving, obtaining, and storing of echo signals is done at a rate thatis higher than a rate of processing the array.

In accordance with another embodiment of the invention, an ultrasoundprocessing method is provided that includes generating an acousticsignal, receiving at least one echo of the acoustic signal at aplurality of receiving elements and obtaining an echo signal therefrom,storing each echo signal from each of the plurality of receivingelements, mapping a given voxel into a region of the stored echosignals, organizing the mapped region of the stored echo signals into anarray for the given voxel, processing the array to generate a signalresponse for the given voxel, and using the signal response to obtainthree-dimensional acoustic information for the given voxel.

In accordance with another aspect of the foregoing embodiment, all ofthe features of the first embodiment described above are applicable tothis second embodiment of the invention.

In accordance with another embodiment of the invention, a method ofprocessing acoustic echoes is provided that includes storing acousticecho signals received from a plurality of receiving elements, mapping agiven pixel into a region of the stored echo signals, organizing themapped region of the stored echo signals into an array for the givenpixel, performing operations on the array to generate a signal responsefor the given pixel, and using the signal response to obtain acousticinformation for the given pixel.

In accordance with another embodiment of the invention, an ultrasoundprocessing system is provided that includes a module adapted to generatean acoustic signal, receive at least one echo of the acoustic signal ata plurality of receiving elements in the module and obtain a pluralityof echo signals therefrom, and means for processing that communicateswith the module and is adapted to map a given pixel into a region ofstored echo signals received from the module, to organize the mappedregion of the stored echo signals into an array for the given pixel, toperform operations on the array to generate a signal response for thegiven pixel, and to use the signal response to obtain acousticinformation for the given pixel.

In accordance with another aspect of the foregoing embodiment, theprocessing means is adapted to initially generate a set of given pixelsin which each given pixel in the set has a known spatial relationship toa receiving element in the module. Ideally, the processing means isconfigured to generate an image from the acoustic information for thegiven pixels in the array. Alternatively or in combination therewith, ameans for displaying an image is provided that receives the signalresponse from the processing means for generating an image on a computerdisplay or in printed form or in other forms known to those skilled inthe art.

In accordance with another embodiment of the present invention, anultrasound processing system is provided that includes a module adaptedto generate an acoustic signal, receive at least one echo of theacoustic signal at a plurality of receiving elements in the module andobtain a plurality of echo signals therefrom, and means for processingthat communicates with the module and is adapted to map a given voxelinto a region of stored echo signals received from the module, toorganize the mapped region of the stored echo signals into an array forthe given voxel, to perform operations on the array to generate a signalresponse for the given voxel, and to use the signal response to obtainacoustic information for the given voxel.

In accordance with another aspect of the foregoing embodiment of theinvention, multiple 2D images planes are displayed as arbitrary slicesof the real-time 3D data set.

In accordance with another aspect of the foregoing embodiment of theinvention, multiple 2D arbitrary image plane slices and a 3D renderingare displayed in real time.

As will be readily appreciated from the foregoing, the benefits ofchanging to a software-based ultrasound system architecture implementedon commercially available computing platforms include:

-   -   Significantly lower cost of hardware.    -   Lower development costs and faster time to market by avoiding        lengthy design cycles for custom integrated circuits (ASICs).    -   Direct leveraging of cost/performance advances in computer        technology.    -   Flexibility for development of many new processing approaches,        in commercial and academic environments.    -   Increased diagnostic capability, based on image quality        improvements, for cost sensitive application areas.    -   Increased utilization of ultrasound in specialty applications        where cost has been a barrier to adoption.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features and advantages of the present inventionwill be more readily appreciated as the same become better understoodfrom the following detailed description of the present invention whentaken in conjunction with the following drawings, wherein:

FIGS. 1A and 1B are schematic representations of a known flow-throughultrasound image formation architecture;

FIG. 2 is a schematic representation of a software-based architecture ofone embodiment of the present invention;

FIG. 3 is a diagram of a plug-in module formed in accordance with oneembodiment of the present invention;

FIG. 4 is a schematic representation of the acquisition data for a 128element linear array formed in accordance with the present invention;

FIG. 5 is an illustration of a pixel mapping process of the presentinvention;

FIG. 6 is an image of target points obtained from a pixel-orientedsimulation of the present invention;

FIG. 7 is an isometric representation of the data from FIG. 6;

FIG. 8 is a side-by-side comparison of two images of target pointsobtained from a pixel-oriented simulation of the present invention;

FIG. 9 is a spatially-compounded image of target points obtained from apixel-oriented simulation of the present invention;

FIG. 10 is an isometric representation of the data from FIG. 9;

FIG. 11 is a block diagram illustrating representative applications forthe pixel-oriented image processing method of the present invention; and

FIGS. 12A-12C illustrate alternative processing methods.

DETAILED DESCRIPTION OF THE INVENTION

The software-based method and system architecture in accordance with oneembodiment of the invention implements all real-time processingfunctions in software. The proposed architecture is shown schematicallyin FIG. 2.

The only custom hardware component in the software-based system is aplug-in module to the expansion bus of the computer that contains thepulse generation and signal acquisition circuitry, and a large block ofexpansion memory that is used to store signal data. The signalacquisition process consists of amplifying and digitizing the signalsreturned from each of the transducer elements following a transmitpulse. Typically, the only filtering of the signals prior todigitization, other than the natural band-pass filtering provided by thetransducer itself, is low pass, anti-aliasing filtering for A/Dconversion. The signals are sampled at a constant rate consistent withthe frequencies involved, and the digitized data are stored in memorywith minimal processing. The straight-forward design of the signalacquisition allows the circuitry to be implemented with off-the-shelfcomponents in a relatively small amount of board area.

A more detailed look at the plug-in module is shown in FIG. 3. Multipleacquisition channels are shown, each composed of a transmitter, receiverpre-amplifier, A/D converter, and memory block. During receive, thetransducer signals are digitized and written directly to the individualmemory blocks. The memory blocks are dual-ported, meaning they can beread from the computer side at the same time acquisition data is beingwritten from the A/D converter side. The memory blocks appear as normalexpansion memory to the system CPU(s). It should be noted that the sizeof the plug-in module is not limited to the normal size of a standardcomputer expansion card, since the system is preferably housed in acustom enclosure. Also, multiple plug-in modules can be used toaccommodate a large number of transducer elements, with each moduleprocessing a subset of the transducer aperture.

The components for the plug-in module, including amplifiers, A/Dconverters and associated interface circuitry, and the needed componentsfor transmit pulse generation and signal acquisition are readilycommercially available components and will not be described in detailherein. The memory block needed for RF data storage of echo signalsobtained from received echoes is essentially the same circuitry as foundin commercially available plug-in expansion memory cards, with theaddition of a second direct memory access port for writing the digitizedsignal data. (The received echo signal data is generally referred to asRF data, since it consists of high frequency electrical oscillationsgenerated by the transducer.) The memory is mapped into the centralprocessor's address space and can be accessed in a manner similar toother CPU memory located on the computer motherboard. The size of thememory is such that it can accommodate the individual channel receivedata for up to 256 or more separate transmit/receive cycles. Since themaximum practical depth of penetration for round trip travel of anultrasound pulse in the body is about 500 wavelengths, a typicalsampling rate of four times the center frequency will require storage ofas many as 4000 samples from an individual transducer element. For asampling accuracy of 16 bits and 128 transducer channels, a maximumdepth receive data acquisition will require approximately one megabyteof storage for each transmit/receive event. To store 256 events willtherefore require 256 MB of storage, and all totaled, a 128 channelsystem could be built on a few plug-in cards.

Another aspect of the software-based ultrasound system is the computermotherboard and its associated components. The motherboard for theproposed design should preferably support a multi-processor CPUconfiguration, for obtaining the needed processing power. A completemulti-processor computer system, complete with power supply, memory,hard disk storage, DVD/CD-RW drive, and monitor is well-known to thoseskilled in the art, can be readily commercially purchased, and will notbe described in greater detail.

A software-based ultrasound system must truly achieve“high-performance,” meaning image quality comparable to existinghigh-end systems, in order to provide a significant benefit to thehealth care industry. This level of performance cannot be achieved bysimply converting the flow-through processing methods of current systemsto software implementations, since a simple addition of all theprocessing operations needed for one second of real-time imaging in theflow-through architecture gives a number that exceeds the typical numberof operations per second currently achievable with several generalpurpose processors. Consequently, new processing methods are requiredthat achieve a much greater efficiency than the flow-through methods.

In one embodiment of the software-based ultrasound system architectureof the present invention, the input data for signal and image processingconsists of the set of RF samples acquired from individual transducerchannels following one or more transmit events. For an example, let usconsider a typical 2D imaging scanning mode with a 128 element lineartransducer array, as shown in FIG. 4.

In this case, a ‘transmit event’ would consist of timed pulses frommultiple transducer elements to generate a plurality of acoustic wavesthat combine in the media to form a focused ultrasound beam thatemanates outwards from an origin point on the transducer at a specificelement location. Multiple transmit events (128 in all) produceultrasound beams that are sequentially emitted incrementally across thewidth of the transducer face, thus interrogating an entire image frame.For each of these transmit beams, the received echo data are collectedfrom each of the 128 receiver elements in the transducer and organizedinto a data array with each column representing the sampled echo signalreceived by the corresponding transducer element. Thus, each array has128 columns, corresponding to the 128 transducer elements, and a numberof rows corresponding to the number of samples in depth that were taken(in this case, we will assume 4096 rows resulting in 4096 samples).These 128 data arrays then constitute an RF data set that is sufficientto produce one complete image frame.

It is worth noting that in the flow-through architecture, the RF dataset described above does not even exist (at least not all at one time),since the beam and image formation takes place as the data streams infrom the transducer. In other words, as the data return to each elementafter a transmit event, they are processed and combined (referred to asbeamforming) to generate a single RF signal representing the focusedreturn along a single beam (scanline). This RF signal is processed(again in real-time) into echo amplitude samples, which are stored in amemory array. When all beam directions have been processed, the echoamplitude data are then interpolated and formatted into a pixel imagefor display. Since all processing takes place in real-time, theprocessing circuitry must be able to ‘keep up’ with the data streamingin from the transducer elements.

In the software-based architecture of the present invention, all inputdata is stored prior to processing. This uncouples the acquisition ratefrom the processing rate, allowing the processing time to be longer thanthe acquisition time, if needed. This is a distinct advantage in highfrequency scans, where the depth of acquisition is short and the samplerate high. For example, a 10 MHz scanhead might have a useable depth ofimaging of around four centimeters. In this case, the speed of sound intissue dictates that each of the 128 transmit/receive events acquire andstore their data in 52 microseconds, a very high acquisition data rate.In the flow-through architecture, these acquisition data would be formedinto scanlines in real-time at high processing rates. In thesoftware-based architecture of the present invention, the storage of RFdata allows the processing to take as long as the frame period of thedisplay, which for real-time visualization of tissue movement istypically 33 milliseconds (30 frames/second). For 128 pixel columns (therough analogy to scan lines), this would allow 258 microseconds ofprocessing time per column, rather than the 52 microseconds of theflow-through architecture. This storage strategy has the effect ofsubstantially lowering the maximum rate of processing compared with theflow-through architecture for typical scan depths.

Pixel-Oriented Processing

The storing of input data reduces the maximum processing rates butdoesn't necessarily reduce the number of processing steps. To accomplishthis, a new approach to ultrasound data processing is taken. The firststep is to recognize that the ultimate goal of the system when in animaging mode is to produce an image on the output display. An ultrasoundimage has a fundamental resolution that depends on the physicalparameters of the acquisition system, such as the frequency and arraydimensions, and can be represented as a rectangular array of pixelvalues that encode echo amplitude or some other tissue (acoustic)property. The density of this rectangular pixel array must provideadequate spatial sampling of the image resolution. It is recognized thatdisplay images need not consist only of rectangular arrays of pixels,but could consist of any arbitrary set of pixels, representing differentgeometric shapes. The next step is to start with one of the pixels inthis image array and consider which sample points in the RF data setcontribute to the calculation of this pixel's intensity, and determinethe most efficient way of accessing and processing them. This approachis a completely different approach than the one utilized by the currentflow-through architecture because only information that contributes topixels on the display needs to be processed. In the approach of thepresent invention, a small region on the display image will take lessoverall processing time than a large image region, because the smallregion contains fewer pixels. In contrast, the flow-through processingmethods must be designed to handle the maximum data stream bandwidths,independent of the image region size.

After processing the pixel array required to adequately represent theultrasound image, the array can be rendered to the computer display atan appropriate size for viewing. The graphics processor of the computer,requiring no additional CPU processing, can typically carry out thisoperation, which consists of simple scaling and interpolation.

We next consider the processing strategy for a single pixel of ourultrasound image. In this discussion, we will assume that our objectiveis to obtain the echo intensity at the corresponding spatial location ofthe pixel with respect to the transducer array. Other acousticparameters may be similarly obtained. Our first step is to find theregion of acquisition RF data containing samples that contribute to theecho intensity calculation. To accomplish this for the scanning methodof FIG. 4, we first find the acquisition scan line that comes closest tointersecting the pixel location, and then use the correspondingindividual element data array. FIG. 5 shows this mapping process for anexample pixel in an ultrasound image. In FIG. 5, the indicated pixelmaps to the closest acquisition line of the scan, which in this case isscan line 4, whose RF data resides in the fourth individual element RFdata array (which represents data collected from the fourthtransmit/receive event). More than one RF data array could be chosen ascontributing to the pixel signal, but for this example we will consideronly a single data array.

Out next step is to map out the region in the individual element arraycontaining samples that contribute to the pixel's intensity calculation.This mapping process is fairly complex and depends on several factors.The transducer elements each have a region of sensitivity thatdetermines how they will respond to a signal returning from a particularpoint in the image field. For a given image point, only elements thathave sensitivities above a predetermined threshold need be considered,since if the sensitivity is too low, an element will not contributeuseful information to the pixel's quantity. This sensitivity thresholdthen determines the number of element data columns to include in themapped region. As shown in FIG. 5, elements on the far right hand sideof the transducer are not included in the mapped data region.

The starting depth of the mapped data region is determined by thearrival time of the returning echo at each individual transducerelement. As shown in FIG. 5, the image point signal for elements furtheraway from the image point is captured later in time, and so the startingpoint of the data set is deeper in memory. Finally, the depth rangeneeded for the mapped data region is dependent on the duration of thetransmit pulse generated. Longer transmit pulses will excite the imagepoint for a longer period of time, generating echo signals that extendover a larger depth span of the RF memory.

Fortunately, many of the factors that go into determining the region ofmapped data can be pre-computed for a given pixel grid, since this griddoes not change over the multiple frames of a real-time image sequence.Using pre-computed factors, the mapped data region for a given pixel canbe rapidly and efficiently determined, saving considerable computationsduring real-time imaging.

After selecting out the pixel mapped RF data, we can organize it into amatrix, RFP_(nm), as shown below.

${RFP}_{nm} = \begin{bmatrix}{a_{11}a_{12}\mspace{14mu}\ldots\mspace{14mu} a_{1\; k}} \\a_{21} \\\ldots \\\ldots \\{a_{j\; 1}\mspace{14mu}\ldots\mspace{14mu} a_{jk}}\end{bmatrix}$

The notation ‘P_(nm)’ refers to the image pixel in row n, column m. Thematrix columns are the vertical bars of FIG. 5 where it is assumed thatthe number of samples, j, in each vertical bar are the same. The numberof samples, j, is dependent on the range of RF data in time needed forcapturing the signal generated by the transmit pulse. The index, k, isthe number of channels in the RF data array that have adequate signalstrength from to the image point to participate in the intensitycalculation.

The process of computing the signal intensity value of pixel P_(nm) nowconsists of a series of matrix operations that eventually lead to asingle value. When the computations are organized in this fashion, itquickly becomes apparent that some of the matrix operations may bealgebraically combined, leading to fewer computational operations.Without going into specific details, the operations of sampleinterpolation to find the correct delay values for individual elements,bandpass filtering, Hilbert transform filtering for quadraturedetection, and final summation can be performed in a single matrixmultiply, then taking the trace of the resulting matrix (The trace of amatrix is the sum of the elements along the main diagonal. Since onlythe main diagonal of the result of the matrix multiply is needed, themultiply operation can be considerably simplified). Since many of thematrices needed for these operations are independent of the pixellocation, they can be pre-computed prior to real-time operation. Theprocessing matrix can then be formed by combining pre-computed elementswith elements that change dynamically with the pixel location (such asinterpolation parameters). With a fixed number of interpolation steps,it is even possible to select the rows of the processing matrix from acollection of pre-computed vectors. The use of pre-computed data forforming the processing matrix, while not essential to the method, cansubstantially reduced processing time for real-time operation.

The signal value derived from the pixel oriented processing is typicallya complex signal value, which can be represented by quadrature samplesI, and Q. To obtain the echo intensity at our image point, the magnitudeof the signal is computed, using a simple square root of the sum of thesquares of the quadrature samples. If phase information is needed (asfor additional processing for Doppler sensing), the complex signalrepresentation can be retained.

With this computational approach, the number of processing stepsrequired to compute a pixel's reconstructed signal value are reducedsubstantially over the flow-through architecture. Estimates derived fromsample calculations indicate that for typical image sizes, operationreductions as great 10-to-1, a full order of magnitude, are possible.Moreover, the matrix operations needed can be carried out using thevector processing capabilities of modern processors, where multiple datacan be operated on using single instructions (These instructions arecalled ‘SIMD’ instructions, which stands for ‘single instruction,multiple data.’ For example, the Altivec processing unit of the PowerPCcan perform a multiply and accumulate on two vectors, containing eight16-bit samples each, in a single clock cycle). These factors make itfeasible to perform real-time processing of ultrasound image data usingone or more general-purpose processors.

It is important to note that for the typical imaging scan, the pixeloriented processing method generates no intermediate data sets—theprocessing method goes directly from unprocessed acquired RF data topixel intensity, through a series of matrix operations on the mappedacquisition data. Each pixel of the output image maps to its own uniqueregion of the acquisition data, and has its own processing matrix,allowing a direct conversion from raw acquisition data to the desiredacoustic signal estimate. This is not the case with the traditionalflow-through architecture, which typically processes the individualchannel RF data to beamformed RF samples along transmit/receive raylines and then generates a detected amplitude data set that is then scanconverted for display. In the pixel-oriented processing method, even theprocess of scan-conversion, which for a sector format scan involvespolar-to-rectangular coordinate conversion, is included in the singleprocessing operation.

For irregular shapes of image data, it is more appropriate to considerthe collection of pixels to be rendered as a pixel set. The actualdisplay presented to the user can then consist of multiple pixel setsprocessed and rendered as a display frame. This concept is useful forimplementing complex scan formats, as well as the various standard modesof ultrasound scanning, such as 2D imaging combined with Dopplerimaging, 2D imaging combined with time-motion imaging (M-mode), or 2Dimaging combined with spectral Doppler display. In the case oftime-motion imaging and spectral Doppler, the pixel set might consist ofa single pixel column, which is moved sequentially across the display.

It should also be noted that the pixel-oriented processing methodgenerates image data that can be precisely measured on the display toderive other types of empirical data. In 2D imaging, each pixel has aknown spatial relationship to the transducer, consequently, ameasurement distance in pixels can be easily converted to a measurementdistance in the media being imaged.

One possible impediment to the processing method described above is busbandwidth. The memory arrays of received RF data associated with eachtransmit event must be accessed to compute image points and this accessmust occur over the expansion bus of the computer. If, for the case of amaximum range ultrasound acquisition, all samples in each memory arraywere needed for processing, the required bandwidth for the samplingmethod described above would be 128×4096×(2 bytes/sample)×(128arrays)=128 MBytes per frame (The second level caching of accessedsamples insures that samples needing multiple times for processing in agiven frame will be assessed from the cache after the first access,rather than over the expansion bus). At 30 fps, this would amount to arather large bandwidth of 3.75 GBytes/second, which is at the limits ofthe current capabilities of most computer buses (the PCI-Express bus isspecified at a peak data rate of 256 KBytes/sec/lane, which for a 16lane expansion slot provides 4 GBytes/sec of transfer capability).Fortunately, due to the factors explained above, only a subset of thesamples in each memory array are needed to compute the image points.Since each transducer element has a limited spatial range ofsensitivity, not all elements contribute to a given reconstructionpoint. Moreover, the typical round trip imaging range for mostapplications is around 500-600 wavelengths (for example, 8-10 cm for a 5MHz transducer), so that the memory arrays are only partially filled.These factors result in a typical bus bandwidth requirement of around1-2 GBytes for 30 fps imaging, which is well within the capabilities ofcurrent computer expansion buses.

A further reduction of bus bandwidth can be accomplished by using fewertransmit events, which amounts to a type of multi-line imaging—atechnique that is commonly used on high-end ultrasound systems toimprove frame rate. Since the transmit beam can be broadened to coverthe width of the image field with fewer transmit/receive events, thenumber of individual element data arrays can be reduced. In this case,multiple pixels along a row fall within the beam pattern of a singletransmit. These multiple pixels will still have their own mapped dataregion, but the regions will all be from the same data array, thusreducing the amount of data that must be transferred over the bus. Thepixel-oriented processing method can easily accommodate this type ofimage acquisition and processing.

Simulation studies have been performed to address the image quality andcomputational speed of the pixel-oriented processing method. An image ofsimulated point targets arranged in a pattern is shown in FIGS. 6 and 7.The linear transducer array simulated is composed of 128 elements at 1wavelength spacing. Since the simulation is in wavelength units, it isindependent of the ultrasound center frequency. The transmit pulse usedin this simulation is a cosine weighted three cycle burst, which is afairly typical pulse shape for current transducers. The transmit focusis set at 100 wavelengths, and accounts for the increased intensity ofthe echo amplitudes around this range. The spacing of image points inthe simulation is at one-wavelength intervals, which is adequate torepresent the spatial resolution of this reconstruction. FIG. 7 shows aperspective view of a zoomed region of FIG. 6 (50 to 130 wavelengths indepth, 32 to 96 wavelengths laterally. The ability to reconstructenhanced views of a sub-region of the image field is another strength ofthe pixel-oriented processing technique.

To generate larger image sizes for high-resolution displays, theultrasound image can be interpolated to larger display sizes using theprocessing capability of the computer's graphic card, requiring noadditional CPU processing. This process is illustrated by referring tothe image in FIG. 6, which contains only 18560 image points, but hasbeen interpolated to a much larger number of pixels (300 pixels perinch) for rendering on the page.

These simulation studies have verified both the accuracy and speed ofthe pixel-oriented processing method. It is important to note that allof the processing functions of an ultrasound imaging system have beenimplemented, including the complex process of beamforming. Furtheroptimization of the processing algorithms can yield higher processingrates, allowing for more complex processing or the rendering of morepixels per image. In addition, the doubling of processor speed withevery 18 months will provide a significant boost to pixel processingrates.

The software-based architecture of the present invention opens up thepossibility of supporting transducers constructed with non-conventionalmaterials and methods, such as low-cost plastic polymer transducers. Itaccomplishes this by completely de-coupling the acquisition process fromthe signal and image formation processing. With a minor change to the RFdata storage memory interface, the memory writes can be changed toread-modify-writes, allowing input data to be summed with data alreadyin memory. This change allows RF signals to be averaged over multipleidentical transmit events, to reduce the effects of system noise andimprove dynamic range. Averaging the RF signals permits significant SNRgains compared with averaging amplitude images.

Much of the noise in ultrasound systems is a result of thermal andradiated digital noise from the electronics of the system. The remainingnoise is usually environmental RF noise, picked up by the transduceracting as an antenna. In both cases, the noise spectrum is fairly flat,so that with the system filtering of the RF signals, the noise appearsas band-limited white noise. This noise typically determines the maximumgain that can be applied to an input signal and thus the penetration ofthe system, as the returning signals are attenuated as they travelincreasing distances through the body.

As mentioned above, the use of signal averaging with the new systemarchitecture can improve signal-to-noise and thus dynamic rangesignificantly. For shallow depths, such as below four or fivecentimeters, it is feasible to use multiple transmit events for eachultrasound beam direction. The round trip travel time of a pulsetraveling to a four centimeter depth is only 52 microseconds, allowing16 transmit/receive cycles in 832 microseconds. Since movement in thebody (with some exceptions) is typically below two or three cm/sec, anecho interface will only move by a small fraction of a wavelength(approximately 1/16 wavelength at 5 MHz) in the time it takes to acquirethe data for these 16 pulses. A full ultrasound frame using 128 beampositions would then take 106 milliseconds to acquire, giving a useableframe rate of 10 frames per second. This method of acquisition would beexpected to result in a four times improvement in signal-to-noise, orabout 12 dB. This improvement in signal-to-noise has been verified insimulation studies. FIG. 8 shows two simulated images processed usingthe pixel-oriented processing method. The image on the left is derivedfrom RF data with one transmit pulse per beam, where band-limited whitenoise approximately 8 times the point target signal strength has beenadded in each channel. The image on the right uses the same signal tonoise ratio for the RF data, but is derived from the average of 16separate transmit/receive events per beam direction.

The implementation of signal averaging in the acquisition of transducersignals should result in sensitivity and penetration improvements nomatter what transducer material is used. For example, it couldfacilitate the utilization of arrays made from micro-electromechanicalsilicone devices, which utilize tiny silicone drums to transmit acousticinformation. Finally, for typical transducers made using PZT, it shouldalso allow acoustic power levels to be reduced without sacrificingimaging performance in conventional exams.

Another benefit of low power, high dynamic range ultrasound imaging maybe in the use of micro-bubble contrast agents to improve visualizationof blood flow. Typical power levels result in the rapid destruction ofthe micro-bubbles, thus limiting visualization studies. Lower powerlevels should provide longer contrast lifetimes and may permit newclinical protocols.

The flexibility of the new software-based ultrasound architectureprovides other advantages over the standard flow-through architecture.Previously, we have described how the new pixel-oriented processingmethods can be used to implement standard ultrasound imaging acquisitionmodes. Since individual channel RF data are captured in memory,alternate modes of ultrasound imaging can also be supported. Asignificant example is often referred to as the ‘uniform illuminationimaging method,’ or ‘flash transmit method.’ In this approach, theentire image field is interrogated at once with a single, unfocusedtransmit pulse, followed by acquisition of the returned echo signalsfrom each individual element in the transducer array into a memorybuffer. With suitable processing of the individual element data, anentire image plane can be reconstructed, without the need for furthertransmit pulses. The flash transmit technique can therefore acquire afull image in the same time it takes to acquire a single scan-line usingthe conventional method, providing theoretical frame rates as much as128 times higher than a typical scan.

The human eye has a fairly slow response time, and as a result, there isnot much benefit for ultrasound imaging display rates beyond around 30frames per second. There are applications, however, such as pediatriccardiac imaging and analysis of heart valve motion, where it isdesirable to have a much higher acquisition rate. To serve theseapplications, the flash transmit imaging technique can be used toacquire RF data frames, which can be stored in successive memorylocations at a high acquisition rates in real-time. For real-timeviewing, frames can be selected out of the acquisition stream at a lowerrate for processing and display. When the scanning is stopped, allacquisition frames in memory can then be processed and played back atnormal or reduced viewing rates, allowing full slow-motion analysis ofthe rapid tissue movement.

As one might expect, there are some disadvantages to the flash transmitimaging technique. Since the transmit pulse is unfocused, there willobviously be some loss in spatial resolution, although this loss will beconfined to the lateral spatial dimension only. Also, since the transmitenergy is more diffuse, there will be some loss of echo intensity.Finally, since the larger echo targets in the image are seen ‘all thetime,’ instead of only along specific scan-lines, a high dynamic rangereconstruction is required to prevent masking of the smaller echosignals. These deficits have typically led to rejection of the flashtransmit reconstruction approach for normal imaging by ultrasound systemdesigners.

The fact that the high frame rate capability of the flash transmitreconstruction technique can be leveraged to reduce or eliminate many ofthe above-mentioned deficits is often overlooked. In fact, the highframe rates possible with this approach open the door to substantialimprovements in contrast resolution, tissue differentiation, and bloodflow imaging that are not possible with the conventional image method.For example, recovery of lateral spatial resolution and substantialimprovements in contrast resolution can be obtained using spatialcompounding with the flash transmit method. The unfocused transmit pulsecan be steered through multiple angles to interrogate the media targetsfrom several directions in a time period short enough not to introducemotion artifacts. The images from the individual steering angles arethen combined to produce a composite image. Even using as many as ninedifferent angles requires only nine transmit pulses, which for a 10 cmimage depth example takes only 1.2 milliseconds. Spatial compounding hasbeen shown to provide significant contrast resolution improvements byreducing speckle artifact and averaging out the variations in echointensity with target interface angles. For the unfocused transmit case,spatial compounding also can regain some of the loss in lateral spatialresolution, by folding the much better axial resolution of the pulseinto the lateral direction. Other techniques for improving contrastresolution, such as frequency compounding and harmonic imaging can alsobe employed while maintaining very short acquisition times.

FIG. 9 shows a simulation that demonstrates the capability of the newsystem architecture for performing spatial compounding using uniformillumination imaging. The spatial compounding uses five steering anglesspaced at 10 degree intervals. Comparing the spatially compounded imageof FIG. 9 with the “scanline” image of FIG. 6, we see that theresolution is comparable while the side lobe levels are somewhat higher.This is remarkable in light of the fact that the acquisition time forthe flash transmit image is roughly 1/25^(th) of the time for theconventional image. The lowest side lobe levels of FIG. 10 are diffuseand distributed, which is desirable for minimizing artifacts in theimage. In actual living tissue, the spatially compounded image wouldshow other benefits, reducing the angular dependence of target returns,and lowering speckle artifact. The flash transmit spatial compoundingimaging method could yield higher tissue differentiation thanconventional imaging at high frame rates, a combination that is notavailable in current high-end systems.

The short acquisition times of the flash transmit imaging method can beleveraged in other ways. Since the new system architecture providesmultiple RF storage buffers, for the flash transmit method thisrepresents multiple complete frames of data, and very high frame ratesfor short imaging sequences are possible. Such sequences may haveimportant novel uses, such as 1) capturing full frame Doppler data atmultiple angles for angle corrected color flow imaging, 2) shear waveimaging, where the propagation of shear wavefronts through a medium canbe visualized, providing information on tissue mechanical properties, 3)elastography, where the strain response of tissue to an external forcecan yield information about tissue stiffness.

Furthermore, the access to a large buffer of RF frame data makesdevelopment of new algorithms straightforward, especially in theacademic research community, which has been hampered by the lack ofaccess to RF data on a clinical machine. The simple ability to trade offframe rate for dynamic range or signal to noise ratio may be a usefulenhancement not easily implemented in a conventional ultrasound system.

FIG. 12 summarizes the variations in the pixel oriented processingmethod as described above. FIG. 12A shows the combining of received echosignals with signals that have been previously stored in the storagearrays. This allows functions such as signal averaging of multipletransmit-receive acquisitions to enhance and improve signal-to-noise anddynamic range of the received signals. FIG. 12B illustrates the methodof combining processed pixel signals from multiple transmit-receiveacquisitions to enhance some aspect of the pixel signal. In the textabove, this method was used for combining image data fromtransmit-receive acquisitions that interrogate media targets fromvarious angles. This results in a spatial compounding that improves thecontrast resolution of the final image. Finally, FIG. 12C illustratesthe de-coupling of the processing of pixel data sets or image framesfrom the acquisition process. In this case, the acquisition signalsrequired to produce an image are grouped into data sets, which consistof one or more acquisition signal arrays. The storage area is made largeenough to store many of these data sets, which can be written to in acircular manner. In this method, the acquisition of echo signal data canbe performed at a high rate limited only by speed of soundconsiderations, while the processing of pixel signals proceeds at alower rate suitable for display. When the acquisition is stopped, alldata sets can be processed at a lower rate to provide a slow motiondisplay.

FIG. 11 illustrates a representative selection of pixel-orientedprocessing applications, which is divided into two areas—high frame rateimaging, which can be used for 3D volume imaging, shear wave imaging,elastography, high dynamic range imaging, and high contrast resolutionimaging, and the second area of high frame rate Doppler flow imaging,which can be used in 3D Doppler flow imaging, vector Doppler flowimaging, and high frame rate tissue Doppler imaging. Furtherapplications in selected categories are also shown in FIG. 11.

The high frame rate applications leverage the pixel-oriented processingmethod combined with uniform illumination or flash transmit techniques.For 3D volume imaging, the entire volume of interest can be interrogatedwith one or more unfocused flash transmit pulses, allowing highreal-time frame rates to be achieved, even with the combination ofmultiple frames for spatial or frequency compounding. For elastographyimaging, the high frames rates allow the imaging of mechanical shearwaves propagating through the image field, which can reveal informationon the elastic properties of tissue. The high dynamic range and highcontrast resolution imaging potential has been discussed above, andleverages the signal averaging and multi-frame processing capability ofthe pixel-oriented processing method.

The pixel-oriented processing method for 3D volume imaging is moreappropriately called a voxel-oriented processing method. This is due tothe fact that the output of a 3D volume scan is typically athree-dimensional cuboid containing volume elements, or voxels. Theprocessing procedure for determining acoustic information about aspecific voxel is the same as for individual pixels in a 2D image. Thevoxel's spatial location is mapped to a region of acquired RF data whichcontributes to the voxel's quantity, and a data matrix is formed. Thedata matrix is then processed using matrix operations to yield thequantity for the voxel. Voxel data over multiple acquisitions can alsobe used to obtain 3D Doppler information.

The voxel data can be displayed as two-dimensional slices through theimaging volume, or as volume-rendered perspective views. It is alsopossible to have simultaneous displays, where the 3D volume rendering isdisplayed along side one or more two-dimensional slices determined bythe system or user. Such displays are possible, since the received echosignal data can be processed with both pixel-oriented and voxel-orientedmethods at the same time.

3D imaging requires more complex transducer arrays, such as mechanicallyswept linear arrays, or 2D arrays with large numbers of elements. Inthis case, the acquisition hardware may require modification. To connecta large number of transducer elements to a lesser number of transmit andreceive channels, analog and/or digital multiplexing is generallyemployed. Some or all of this multiplexing is sometimes incorporatedinto the transducer housing. The multiplexers are used on transmit toselect elements for forming one or more transmit beams that illuminatethe 3D volume. On receive, the multiplexers are used to connect a groupof transducer elements to the available receive acquisition channels. Insome cases, it is appropriate to use synthetic aperture techniques tocombine receive data from multiple acquisition events, thus increasingthe effective number of processing channels.

The right hand side of FIG. 11 shows high frame rate Doppler flowimaging methods that also make use of the flash transmit method combinedwith pixel-oriented processing. It is possible to acquire flowinformation for the entire imaging field with only a small number oftransmit/receive cycles. This ‘ensemble’ of acquisitions can be used tocompute the average rate of change of phase at each pixel location,which is representative of the Doppler frequency shift associated withmoving blood cells. Here again, the high frame rates that can beachieved using this method make practical such applications as 3D volumeflow imaging, vector Doppler flow imaging (detecting both the magnitudeand direction of blood flow), and tissue Doppler imaging (using theDoppler shift produced by low echogenicity moving tissue to enhancevisibility). The high frame rate visualization of tissue motion alsosupports elastography imaging, which seeks to determine the elasticproperties of tissue by observing their response to an inducedmechanical displacement.

It is understood that the pixel and voxel oriented processing methodscan be applied to many additional modes and applications of ultrasoundimaging than are described above. Therefore, the descriptions above arenot intended to limit the scope of the processing method, but rather areprovided to illustrate how the method can be used to support variousexisting and new potential applications.

All U.S. patents, U.S. patent application publications, U.S. patentapplications, foreign patents, foreign patent applications andnon-patent publications referred to in this specification and/or listedin the Application Data Sheet, are incorporated herein by reference, intheir entirety.

From the foregoing it will be appreciated that, although specificembodiments of the invention have been described herein for purposes ofillustration, various modifications may be made without deviating fromthe spirit and scope of the invention. For example, the processingoperations described above to generate pixel or voxel acousticinformation have been implemented using matrix operations, but it isrecognized that standard mathematical operations, or even hardware basedprocessing methods could be used to accomplish some or all of theprocessing steps. Accordingly, the invention is not limited except as bythe appended claims.

1. An ultrasound processing method, comprising: generating an acousticsignal by an ultrasound transducer; receiving at least one echo of theacoustic signal at each of a plurality of receiving elements on theultrasound transducer and obtaining an echo signal from each receivingelement; organizing and storing each of the echo signals into a separatedata set array sufficient to produce an image frame or portion thereofin which each column of each data set array represents the echo signalsobtained from the corresponding transducer element; mapping a givenpixel from a set of pixels into a region of one or more of the data setarrays of the stored echo signals; organizing the mapped region of thestored echo signals into a matrix for the given pixel; processing thematrix of the mapped region of the stored echo signals with a matrixoperation to generate a signal response for the given pixel; and usingthe signal response to obtain acoustic information for the given pixel.2. The method of claim 1, further comprising an initial step ofdetermining the set of pixels that represent an area in a field of viewof the transducer generating the acoustic signal, in which every pixelin the set has a known spatial relationship to the plurality ofreceiving elements.
 3. The method of claim 2, further comprisinggenerating an image from the acoustic information for the pixels in theset.
 4. The method of claim 2, further comprising using the acousticinformation to measure and display spatial data.
 5. The method of claim2, further comprising using the acoustic information to measure anddisplay temporal data.
 6. The method of claim 2, further comprisingusing the acoustic information to measure and display blood flow data.7. The method of claim 2, further comprising measuring and displayingtissue displacement response to induced mechanical displacement causedby an acoustic signal.
 8. The method of claim 1, further comprisinggenerating a plurality of acoustic signals, receiving echoes from theplurality of acoustic signals, combining the received echoes overmultiple generating and receiving cycles to enhance acoustic informationobtained therefrom.
 9. The method of claim 8, wherein the receivingechoes from the plurality of acoustic signals comprises obtaining echosignals from the received echoes, storing the echo signals, andcombining the received echoes comprises combining the stored echosignals into data sets, and further comprising averaging the combinedstored echo signals.
 10. The method of claim 9 wherein the signalresponse comprises an average of the echo signals.
 11. The method ofclaim 8 wherein the received echoes are stored at a rate that is higherthan a rate of processing the array.
 12. The method of claim 1, furthercomprising combining results of multiple cycles of generating acousticsignals, receiving echoes, obtaining echo signals from the receivedechoes, storing the echo signals into data set arrays, and processingthe echo signals to obtain pixel signals to derive enhanced acousticinformation.
 13. The method of claim 12, further comprising processingthe stored echo signals in multiple processing steps and combining theprocessing results to obtain further enhanced acoustic information. 14.The method of claim 13 wherein the enhanced acoustic informationcomprises spatial compounding that improves contrast resolution of afinal image.
 15. The method of claim 13 wherein the enhanced acousticinformation comprises a signal response representative of Dopplerinformation associated with moving tissue or moving blood cells.
 16. Anultrasound processing method, comprising: generating an acoustic signalby an ultrasound transducer; receiving at least one echo of the acousticsignal at each receiving element of a plurality of receiving elements onthe ultrasound transducer and obtaining an echo signal from eachreceiving element therefrom; storing each of the echo signals to form aseparate data set array sufficient to produce an image frame or portionthereof in which each column of each data set array represents the echosignals obtained from the corresponding transducer element; mapping agiven voxel from a set of voxels into a region of one or more of thedata set arrays of stored echo signals; organizing the mapped region ofthe stored echo signals into a matrix for the given voxel; processingthe matrix of stored echo signals from the mapped region for the givenvoxel with a matrix operation to generate a signal response for thegiven voxel; and using the signal response to obtain three-dimensionalacoustic information for the given voxel.
 17. The method of claim 16,further comprising an initial step of determining the set of voxels thatrepresent a region in a field of view of the transducer generating theacoustic signal, in which every voxel in the set has a known spatialrelationship to the plurality of receiving elements.
 18. The method ofclaim 17, further comprising generating a three-dimensional image fromthe acoustic information for the voxels in the set.
 19. The method ofclaim 17, further comprising generating a plurality of acoustic signals,receiving echoes from the plurality of acoustic signals and obtainingcorresponding echo signals, and combining voxel signals obtained fromthe echo signals over multiple cycles of generating, receiving, andstoring to enhance acoustic information obtained therefrom.
 20. Themethod of claim 19 wherein the enhanced acoustic information representsDoppler information associated with moving blood cells or tissue. 21.The method of claim 19, further comprising using the acousticinformation to display directional 3D Doppler flow data.
 22. A method ofprocessing acoustic echoes, comprising: generating echo signals fromacoustic echoes received from a plurality of receiving elements on atransducer; storing each of the echo signals from each receiving elementin a separate data set array in a memory to form a data set sufficientto produce an image frame in which each column of each data set arrayrepresents the echo signals obtained from a corresponding transducerelement; mapping a given pixel from a set of pixels into a region of oneor more of the data set arrays of the stored echo signals; organizingthe mapped region of the stored echo signals into a matrix for the givenpixel; performing matrix operations on the matrix of stored echo signalsfrom the mapped region to generate a signal response for the givenpixel; and using the signal response to obtain acoustic information forthe given pixel.
 23. The method of claim 22, comprising an initial stepof generating the set of pixels chosen to represent an area in a fieldof view of the transducer generating the acoustic signal, in which everygiven pixel in the set has a known spatial relationship to the pluralityof transducer receiving elements.
 24. The method of claim 23, furthercomprising generating an image from the acoustic information for thegiven pixels in the set.
 25. An ultrasound processing system,comprising: a module adapted to generate an acoustic signal, to receiveand process at least one echo of the acoustic signal at each receivingelement of a plurality of receiving elements in the module to generate aplurality of echo signals therefrom, the module including a memorystructured to store each of the plurality of echo signals in at leastone separate data set array sufficient to produce an image frame orportion thereof, in which each column of each data set array representsthe echo signals obtained from a corresponding module element; and aprocessor structured to communicate with the module and to map a givenpixel from a set of pixels into a region of the stored echo signals inone or more of the data set arrays, to organize the mapped region of thestored echo signals into a matrix for the given pixel, to perform matrixoperations on the matrix of stored echo signals from the mapped regionto generate a signal response for the given pixel, and to use the signalresponse to obtain acoustic information for the given pixel.
 26. Thesystem of claim 25 wherein the processor is structured to generate theset of pixels that represent an area in a field of view of the module inwhich each given pixel in the set has a known spatial relationship tothe plurality of receiving elements in the module.
 27. The system ofclaim 26, further comprising a display structured to display an imagefrom the acoustic information for the given pixels in the set of pixels.28. The system of claim 26 wherein the processor is adapted to generatefor display an image from the acoustic information for the given pixelsin the set of pixels.
 29. The system of claim 28 wherein the processoris adapted to measure and generate for display spatial data.
 30. Thesystem of claim 28 wherein the processor is configured to measure andgenerate for display temporal data.
 31. The system of claim 28 whereinthe processor is adapted to measure and generate for display blood flowdata.
 32. The system of claim 28 wherein the processor is adapted tomeasure and generate for display tissue response to induced mechanicaldisplacement caused by an acoustic signal.
 33. The system of claim 26wherein the processor is configured to generate a plurality of acousticsignals, receive echoes from the plurality of acoustic signals andobtain echo signals therefrom, store the echo signals, and combine thestored echo signals into a plurality of data set arrays over multiplecycles of generating, receiving, and storing to enhance the acousticinformation obtained therefrom.
 34. The system of claim 33 wherein thecombined echo signals are averaged.
 35. The system of claim 33 whereinthe signal response generated from processing the matrix of stored echosignals from the mapped region comprises an average of the stored echosignals from the mapped region.
 36. The system of claim 26 wherein theprocessor is adapted to perform multiple steps of processing the matrixand combining the results of the multiple steps of processing to obtainfurther enhanced acoustic information.
 37. The system of claim 36wherein the further enhanced acoustic information comprises spatialcompounding that improves contrast resolution of a final image.
 38. Thesystem of claim 36 wherein the further enhanced acoustic information isrepresentative of Doppler information associated with moving tissue ormoving blood cells.
 39. The system of claim 36 wherein the processor isstructured to receive, obtain, and store echo signals at a rate that ishigher than a rate of processing the matrix of stored echo signals fromthe mapped region.
 40. An ultrasound processing system, comprising: amodule adapted to generate an acoustic signal, to receive and process atleast one echo of the acoustic signal at each receiving element of aplurality of receiving elements in the module to obtain a plurality ofecho signals therefrom, the module including a memory structured tostore each of the plurality of echo signals in at least one separatedata set array sufficient to produce an image frame or portion thereofin which each column of the data set array represents the echo signalsobtained from a corresponding module element; and a processor structuredto communicate with the module and to map a given voxel from a set ofvoxels into a region of one or more of the data set arrays of storedecho signals received from the module, to organize the mapped region ofthe stored echo signals into a matrix for the given voxel, to performmatrix operations on the matrix of stored echo signals from the mappedregion to generate a signal response for the given voxel, and to use thesignal response to obtain acoustic information for the given voxel. 41.The system of claim 40 wherein the processor is structured to determinethe set of voxels chosen to represent a volume in a field of view of themodule in which each voxel in the set has a known spatial relationshipto the plurality of receiving elements in the module.
 42. The system ofclaim 41, further comprising a display device structured to display animage from the acoustic information for the given voxels in the set. 43.The system of claim 41 wherein the processor is adapted to generate animage from the acoustic information for the given voxels in the set ofvoxels.
 44. The system of claim 43 wherein the processor is adapted tomeasure and generate for display spatial data.
 45. The system of claim43 wherein the processor is configured to measure and generate fordisplay temporal data.
 46. The system of claim 43 wherein the processoris adapted to measure and generate for display blood flow data.
 47. Thesystem of claim 43 wherein the processor is adapted to measure andgenerate for display tissue response to induced mechanical displacementcaused by an acoustic signal.
 48. The system of claim 41 wherein theobtained echo signals are combined over multiple cycles of generating,receiving, and storing to enhance the acoustic information obtainedtherefrom.
 49. The system of claim 48 wherein the combined echo signalsare averaged.
 50. The system of claim 48 wherein the signal responsecomprises an average of the stored echo signals from the mapped region.51. The system of claim 41 wherein the processor is structured toperform multiple steps of processing the matrix of stored echo signalsin the mapped region and combining the results of the multiple steps ofprocessing to obtain further enhanced acoustic information.
 52. Thesystem of claim 51 wherein the further enhanced acoustic informationcomprises spatial compounding that improves contrast resolution of afinal image.
 53. The system of claim 51 wherein the further enhancedacoustic information is representative of Doppler information associatedwith moving tissue or moving blood cells.
 54. The system of claim 51wherein the processor is structured to receive, obtain, and store echosignals at a rate that is higher than a rate of processing the matrix.